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Nokia CTO wades in on physical AI

Artificial intelligence is shifting from abstract models to tangible systems that interact with the physical world. As the global AI ecosystem pivots toward real-world integration-from smart robotics to embedded intelligent systems-the race to define standards and infrastructure intensifies. This evolution has introduced a transformative domain: Physical AI, where digital cognition meets physical execution. These AI agents don't just compute; they move, sense, adapt, and even self-correct in dynamic environments.

Into this arena steps Nokia's Chief Technology Officer, issuing a decisive perspective on what Physical AI means for future networks, resilient systems, and cyber-physical security. The statement marks a pivotal moment as telecom and tech leaders recalibrate strategies to manage both the scale and risk of increasingly autonomous systems.

In this post, we'll unravel how Nokia's vision positions it at the nexus of AI, connectivity, and secure infrastructure. We'll examine the deeper implications of Physical AI for industries, explore what Nokia's stance signals to global tech stakeholders, and trace how AI embodied in machines challenges conventional approaches to cybersecurity and network design.

Who Shapes Nokia's Tech Vision? Meet the CTO Behind Its AI Strategy

Name and Background

Amidst a changing digital landscape, Nishant Batra holds the pivotal position of Chief Strategy and Technology Officer (CSTO) at Nokia. Appointed to the role in January 2021, Batra brings a global perspective rooted in deep technical expertise and strategic foresight. Born in India, he holds a Master's degree in Telecommunications and an MBA in International Business, both of which support his unique skillset-an intersection of engineering and enterprise.

Industry Experience and Career Highlights

Before joining Nokia, Batra served as the Chief Technology Officer at Veon and spent over a decade at Ericsson, where he led various functions across R&D, product management, and strategy. His portfolio includes leadership in future network architectures, digital services, and the industrialization of advanced connectivity. At Veon, he played a critical role in overhauling the operator's technology direction across its international markets, developing transformative digital platforms.

Leadership in the Age of Industry 4.0

Batra's vision stands distinctly aligned with the objectives of Industry 4.0. Under his direction, Nokia has sharpened its emphasis on automation, AI-driven networks, and secure, real-time data ecosystems. He leads Nokia Bell Labs' innovation agenda, focusing on digital twins, edge computing, and intelligent systems. His leadership is steering Nokia toward becoming not just a telecom company, but a core enabler of industrial digitalization across sectors like manufacturing, logistics, energy, and smart cities.

Perspectives on AI and Robotics

Commenting on the intersection of robotics and network infrastructure, Batra stated, "We are not building networks only for people anymore; we're building networks for machines." This line encapsulates his commitment to re-engineering telecom frameworks to support an emerging paradigm-the physical augmentation of intelligence. In discussing AI's role in automation, he emphasizes the convergence of digital and physical systems as a necessity, not a novelty.

His stance isn't passive. With deliberate clarity, he pushes for architectures that support self-organizing networks and embedded intelligence. AI, under his framework, is not a supplementary layer. It becomes an intrinsic, operational component-from traffic prediction to autonomous network healing.

How does this leadership translate to action? Nokia, under Batra, has restructured its approach to AI integration, scaling innovation through partnerships, R&D investments, and cross-industry alliances. This strategy is fueling Nokia's transformation from network provider to intelligence pioneer.

Understanding Physical AI: Intelligence That Interacts With the Real World

Defining Physical AI

Physical AI refers to artificial intelligence systems embedded into machines that act and interact in the physical world. Unlike traditional AI, which largely processes data in digital environments-such as algorithms embedded in recommendation engines or natural language processing tools-Physical AI has a tangible presence. It consists of intelligent agents capable of sensing, interpreting, and responding to their surroundings through robotic limbs, sensors, or actuated components.

This branch of AI moves beyond abstract computation to augment or automate physical tasks. It brings computational models into forms that alter manufacturing lines, support surgery, move parcels, or assist in construction.

The Distinction: Traditional AI vs. Physical AI

Traditional AI models focus on data analysis, decision-making, and prediction. They work within software ecosystems, deployed in systems like CRM platforms or stock market forecasting tools. Physical AI, on the other hand, involves embedded intelligence within a body. It marries mechanical systems with perception, cognition, and motion.

Picture a factory robot that uses AI to identify parts, decide the most efficient movement, and adjust its grip pressure depending on object fragility. That's Physical AI in action-not just computing, but acting.

Real-World Examples: Intelligent Machines at Work

Physical AI as a Driver of Human-Machine Collaboration

Physical AI systems transform the dynamics between humans and machines. Instead of replacing human effort entirely, it often enhances it. In manufacturing, cobots-collaborative robots-work side by side with human operators, guided by AI to perform precise or repetitive motions with adaptive safety measures.

In surgical procedures, robotic systems with AI capabilities allow surgeons to make micro-adjustments in real-time. These systems interpret force feedback, visual input, and even vocal commands. The partnership increases precision and reduces human fatigue in high-stakes environments.

A New Frontier for Telecommunications and Infrastructure

Telecommunication networks increasingly incorporate Physical AI to optimize infrastructure. Intelligent inspection robots traverse fiber-optic networks to detect degradation. Urban 5G deployment now includes AI-enhanced drones that position small cells efficiently by analyzing physical layouts and environmental variables.

Physical AI enables dynamic infrastructure management-real-time response to equipment failures, autonomous repair scheduling, and even self-healing networks. It feeds directly into a smarter, more responsive telecom ecosystem.

Merging Intelligence and Infrastructure: Nokia's Strategic Focus

Embedding AI Within Modern Telecommunications

Nokia has integrated AI into the foundations of telecom infrastructure. Rather than treating AI as a peripheral tool, engineers embed algorithms directly into network layers. This allows for rapid interpretation of traffic patterns, user behavior, and operational anomalies. In Nokia's 2023 Tech Vision Report, 74% of surveyed telecom executives confirmed deploying AI at the network edge-a deliberate shift that prioritizes speed and contextual relevance in service delivery.

Automating Networks, Predicting Failures, Optimizing Services

Network automation has evolved beyond simple rules-based operations. Nokia's AI-powered systems, particularly through its Cognitive SON (Self-Organizing Networks) platform, reprogram signal routing based on real-time usage and environmental factors. This reduces latency and balances load even during peak hours. In predictive maintenance, AI models ingest terabytes of sensor and log data, identifying root causes of failures before they occur. During a 2022 deployment in Europe, Nokia's predictive system cut unplanned downtime by 35% in metro data centers.

Service optimization now unfolds in milliseconds. AI monitors user sessions, adjusts bandwidth dynamically, and reallocates resources to maintain quality of experience. An internal benchmarking study revealed that video streaming performance improved by 27% after implementing AI-enabled traffic shaping models in 5G testbeds.

Pushing Performance Boundaries with Machine Intelligence

Intelligent machines are enabling Nokia to remake performance benchmarks across wireless and fiber networks. For RF planning, machine learning assesses topographic and infrastructure data to reduce signal loss. In rural deployments, this has extended stable coverage by up to 20 kilometers beyond prior limits. Autonomous radio tuning systems now replace manual adjustments, significantly narrowing the margin of network interference.

At the infrastructure level, AI simulations test hardware within digital twins-virtual models that mirror network conditions. Nokia's 5G Smart Lab in Espoo runs thousands of such simulations daily, compressing R&D cycles while increasing deployment precision. The net result: quicker deployment, fewer field failures, and higher system uptime.

Real-Time Sensing, Thinking, and Action

The transition from reactive to anticipatory networks hinges on real-time AI. Nokia enables microsecond-level data capture from base stations, edge nodes, and user devices. Edge inferencing engines process this data instantly. For example, during a pilot in South Korea, millimeter wave signals from smart lampposts powered by embedded AI successfully guided autonomous delivery robots across urban intersections-decisions were made in under 50 milliseconds.

In core networks, AI controllers fine-tune packet flows as congestion begins to form, not after. This preemptive function improves speed and reliability, especially critical for latency-sensitive applications such as VR streaming and vehicle-to-everything (V2X) communication.

By embedding AI directly into telecommunications architecture, Nokia isn't merely enhancing networks-it's redesigning them from the inside out, setting a new standard for intelligent infrastructure.

Reframing Intelligence: Human-Machine Collaboration at the Core

Nokia's Perspective on Human-Machine Synergy

Nokia's CTO views collaboration between humans and machines as a fundamental shift, not a futurist concept. The company envisions intelligent machines not as replacements but as accelerators of human potential. This translates into AI systems embedded in physical processes working hand-in-hand with people, complementing human intuition with machine precision. Rather than delegating full control, Nokia promotes hybrid models where AI systems handle real-time decision-making yet leave critical thinking and oversight in human hands.

Enhancing Performance Through Physical AI

Physical AI contributes to operational fluidity. In network operations, AI-driven physical systems monitor, diagnose, and adjust network parameters in milliseconds-outpacing traditional engineering responsiveness. This type of collaboration leads to minimized downtime, higher throughput, and adaptive service delivery that aligns with fluctuating demand patterns. Physical AI doesn't merely observe; it interacts physically with environments, using sensors, actuators, and spatial intelligence to augment tasks once reliant on manual labor.

Examples in Action: 5G and Smart Factories

Embedding Human Values in Automated Systems

Delegating decisions to machines introduces complexity beyond performance. Nokia embeds explainability into its AI systems to ensure that automated decisions remain interpretable. During network outages, for example, fault detection systems not only locate anomalies but generate reasoned, traceable logs interpretable by system engineers. This aligns decision automation with traceability and accountability, guarding against opacity in machine reasoning.

The focus remains constant: machines execute with speed and scale, but humans retain command over the narrative and direction. This model reduces friction and amplifies impact across industries deploying Physical AI in real-world operations.

The Role of Robotics in Industry 4.0

The expansion of robotics fueled by AI

Robotics no longer operates in isolation from other technologies. In the context of Industry 4.0, AI has dramatically transformed robotic systems from pre-programmed tools into adaptive agents capable of decision-making. Machine learning algorithms analyze data from embedded sensors in real time, enabling robots to adjust actions dynamically. This shift allows robotic platforms to collaborate, learn from previous tasks, and optimize performance without human intervention.

Robotics in telecom site inspection, installation, and remote troubleshooting

Telecommunications infrastructure demands both precision and resilience. Robotics, guided by AI, is actively replacing manual field tasks with automated operations. Site inspections once requiring personnel to navigate hazardous environments can now be conducted by drones equipped with vision-based recognition systems. Beyond inspection, robots handle antenna installations using real-time spatial mapping and robotic arms calibrated for micrometric precision. In faults or network anomalies, autonomous systems controlled remotely initiate diagnostics, reducing response time and minimizing service disruption.

Nokia's deployment of robotic technologies integrated with their AI frameworks

Nokia integrates robotics into its AI-driven operational model with a focus on edge computing and real-time analytics. One notable deployment involves autonomous ground units navigating complex terrain to inspect and manage rural and urban network assets. These units transmit data to Nokia's Edge Cloud, where AI models, trained on historical maintenance logs and environmental conditions, interpret anomalies. In parallel, the company employs robotic manipulators in assembling network hardware in its manufacturing hubs, streamlining production through AI-tuned robotic sequencing.

Benefits for performance, scalability, and operational efficiency

Security in the Age of Physical AI: New Frontiers, New Threat Models

Redefining the Threat Landscape

Physical AI systems-sensor-rich, mobile, autonomous machines-introduce a multidimensional attack surface. According to Nokia CTO Nishant Batra, the complexity of securing these systems goes far beyond traditional IT infrastructure. With interconnected devices now embedded in critical infrastructure, factories, and transportation networks, vulnerabilities multiply across software, hardware, firmware, and even motion data layers.

Each robotic limb, each edge-processing unit, becomes a potential entry point. Security must no longer be a bolted-on feature-physical AI demands embedded, adaptive defense mechanisms that evolve in real time.

AI-integrated Security: No Longer Optional

Embedding AI-driven security directly into machines is no longer theoretical. These systems execute autonomously, make split-second decisions, and handle mission-critical tasks. Static firewalls and manual oversight cannot scale to this operational tempo. Nokia's approach centers on self-healing, AI-driven security layers that detect, analyze, and neutralize threats within milliseconds.

Encrypted Data Flows at Every Interaction Point

Data flowing across physical AI environments-from onboard modules to cloud processing centers-moves constantly. Nokia prioritizes encrypted communication protocols such as TLS 1.3 and Zero Trust architectures to ensure no data packet travels unverified. Physical-to-digital translation layers use secure schema validation, reducing the risk of injection or spoofing attacks.

Every transfer, from LiDAR scan results to machine diagnostics, is secured using Transport Layer Security and forward secrecy. This ensures that even if credentials leak, historic data transfers remain inaccessible.

Identity Management Systems for a Machine-Centric World

As machine-to-machine communication accelerates, traditional user-based identity management breaks down. Nokia is shifting toward hardware-based, cryptographically anchored identity for devices. Using unique hardware identifiers and public-key infrastructure, machines authenticate each other without relying on centralized directories.

These identity systems enable fine-grained access control based on role, context, and machine state. If a robotic arm begins unexpected behavior outside its assigned zone, the system revokes its permissions dynamically-before damage occurs.

Control Through the Cloud: Partnering with Cybersecurity Titans

Cloud services aren't just operational utilities-they're an integral part of real-time security protocols. Nokia's partnership initiatives with firms like Cloudflare integrate edge protection with centralized telemetry. Cloudflare's global threat intelligence feeds input directly into Nokia's SXi security engine, creating a unified field of view across thousands of industrial endpoints.

From anomaly detection at the edge to encryption key lifecycle management, cloud-based controls create a harmonized defense perimeter that flexes with operational needs. These collaborations don't just secure devices-they transform them into active participants in organizational cyber defense.

Cloudflare's Cybersecurity Role in Nokia's AI-Driven Telecom Vision

Securing the Edge Through Strategic Collaboration

While Nokia leads the charge in integrating physical AI into telecom infrastructure, partners like Cloudflare reinforce that approach with digital armor. Nokia doesn't operate in a vacuum. Collaborations with cloud security leaders directly impact the integrity and availability of telecom services. Cloudflare, in particular, delivers infrastructure-level protection that's critical in an environment where AI-controlled systems depend on uninterrupted, real-time connectivity.

Mitigating Attacks Before They Disrupt Operations

Cloudflare's mitigation of Distributed Denial of Service (DDoS) attacks is not just preventative-it is preemptive. Operating at the network edge, Cloudflare absorbs and filters malicious traffic before it reaches the core infrastructure. In a physical AI scenario managed by telecom networks, even seconds of disrupted service can translate into equipment failure or delayed machine decision-making. Cloudflare's global network, capable of handling over 200 Tbps of traffic, ensures continuity without adding latency.

Protecting the Digital Perimeter in Telecom Environments

Telecom environments, once isolated, now operate in fluid, cloud-integrated ecosystems. The cloud-based AI decision loops, combined with millions of connected devices, extend the digital boundary beyond traditional firewalls. Cloudflare's Zero Trust Network Access (ZTNA) model enforces identity-based access control, allowing only authenticated workflows and applications to interact with critical AI systems. This containment model stops lateral movement of threats, reducing exposure at every layer.

Scrubbing Data at Scale to Protect Service Availability

Cloud-based scrubbing centers function like industrial-grade filters for network data. Cloudflare inspects, sanitizes, and reroutes high volumes of network flows in real time, removing potentially malicious content. For AI applications processing sensory inputs from autonomous vehicles, drones, or industrial robots, this intervention prevents corrupted data from destabilizing downstream algorithms. The result: stabilized operational continuity even under targeted attack scenarios.

Integrated Security Frameworks for AI Deployment

Alignment between Nokia's AI deployments and Cloudflare's security service framework ensures seamless policy propagation across hybrid networks. From endpoint identification to data encryption in transit, every vector aligns under a single command mesh. This shared architecture supports fast AI model iteration cycles without compromising on security hygiene. More importantly, it provides a trusted foundation for Nokia to roll out AI-infused offerings across their global telecom ecosystem.

Maintaining Trust: Ownership, Transparency & Control

Data Ownership and Identity: The Foundations of Trust

In Physical AI, machines don't just process data-many of them physically act in the world based on that data. This elevates the need for strict ownership rights and accurate identity (ID) verification. Nokia asserts that individuals and organizations must retain clear legal ownership of their data. Without it, AI systems embedded in robotics or telecom infrastructure could make unauthorized inferences or take actions with real-world consequences.

ID authentication mechanisms must operate beyond traditional login credentials. Biometric verification, cryptographic identifiers, and decentralized ID protocols are gaining ground. According to a 2023 survey by Juniper Research, decentralized identity solutions are projected to manage over 890 million digital IDs by 2025, driven by the demand in AI-integrated environments.

Transparent AI Decision-Making: Nokia's Model

Nokia advocates that AI-driven systems-especially those managing networks, connected hardware, or robotic agents-must be auditable. That means capturing decision paths, recording input sources, and revealing model behavior in a form that human supervisors can understand.

The company emphasizes the use of Explainable AI (XAI) frameworks in production systems. These provide algorithmic transparency and allow improvement cycles based on user or auditor feedback. Not every AI model qualifies; convolutional networks, for example, require interpretive layers to meet these demands. Nokia's labs are integrating such interpretability features directly into design, not as an afterthought.

Ethical AI in Customer-Facing Interactions

When robots interact with humans-whether in logistics, retail, or telecom support-reliability alone doesn't suffice. Nokia draws a line: AI must behave within agreed ethical parameters when representing companies to end-users. It must not mimic manipulation or generate responses based on deceptive logic models.

Take AI-powered chatbots in customer service. They now handle over 80% of routine telecom queries across major carriers, according to an IBM Global AI Adoption Index. However, those systems must also identify when to transfer a query to a human agent, especially if the dialogue enters regulatory or ethical gray zones.

Consent, Traceability & Accountability: Operational Imperatives

These three areas-consent, traceability, and accountability-form a governance framework Nokia now embeds in AI deployment protocols. They aren't left to ethics boards or future legislation. They're baked into systems from prototype.

Resilience Against Attacks: Building Defensive AI Models

Engineering Resistance: Hardening AI Models Against Adversarial Threats

Defensive AI modeling now leans heavily on adversarial training, randomization techniques, and input filtering mechanisms. Researchers insert carefully crafted malicious data into training sets-forcing systems to recognize and adapt to subtle patterns that signify manipulation. These perturbation-based simulations increase AI's fault tolerance and sharpen anomaly detection. In environments where physical AI operates-sensors, autonomous systems, and telecom-integrated platforms-this training becomes indispensable.

Model immunization also involves proactive measures like gradient masking and robust feature extraction. By deploying techniques such as randomized smoothing, AI platforms withstand carefully tweaked inputs designed to fool them. Architectures now incorporate transformation layers that strip away signal noise commonly used in adversarial tactics-maintaining fidelity and accuracy across classification and decision boundaries.

Cybersecurity Automation Meets Responsive AI

Modern network defense doesn't wait-it reacts in milliseconds. Nokia's approach aligns with dynamic systems that combine automated threat detection with responsive AI layers. These systems monitor traffic patterns, detect variances, and redirect or contain suspected intrusions without human intervention. By embedding AI into cybersecurity stacks, digital environments gain the responsiveness of systems that reconfigure themselves autonomously during attack attempts.

A Secure Interface: Hardware, AI, and Telecom Synergy

From the perspective of Nokia's CTO, true resilience emerges at the intersection of device, model, and network. The integration points-where AI models interact with sensor hardware and 5G networks-represent the most vulnerable surfaces for intrusion. Hardening these junctions requires incorporating trusted hardware roots, encrypted data corridors, and tamper-proof firmware updates. The result is a triple-shielded infrastructure where physical components, digital intelligence, and communication layers synchronize under mutual validation protocols.

In enterprise telecom deployments, firmware-level cryptographic signatures now verify behavioral integrity down to the transistors. AI models periodically hash and check their internal parameters against known baselines, enabling instant rollback in the event of even micro-level corruption. This hardware-backed attestability ensures fidelity across every layer-from chipset to cloud.

Innovating Security: Blocking Breaches Before They Begin

Prevention reduces dependency on reaction. Nokia invests in AI-infused security solutions capable of rendering entire classes of network vulnerabilities obsolete. For example, machine learning-generated honeypots now actively disguise core systems-drawing attackers into isolated environments where their actions are observed, logged, and neutralized in real time.

Public and private network slices-enabled by Nokia's telecom-grade AI-are equipped to operate under zero-trust architecture. This segmentation enforces strict identity verification and behavior monitoring for every user, device, and application entering the infrastructure. By tightening internal perimeters around dynamic microservices and edge nodes, the system restricts lateral movement during a potential breach.

What measures are in place in your infrastructure to isolate AI workloads from hardware-level exploits? Nokia's current tooling offers a blueprint for next-gen physical AI ecosystems capable of resisting sophisticated, multi-layered attacks, even as the threat landscape constantly evolves.

The Future of Technology: Nokia's Vision

Positioning for a New Era of Intelligent Machines

Nokia's long-term roadmap reflects a decisive shift toward a world where Physical AI and Industry 4.0 are not isolated initiatives but deeply interconnected realities. Preparations span infrastructure redevelopment, AI-integrated network systems, and automated service delivery pipelines. The company concentrates on building digital fabric that supports autonomous decision-making in real-time environments-logistics networks, utility grids, and mobility platforms included.

Utilities, Transport, and Urban Intelligence: What Comes Next?

Looking ahead, Nokia's CTO predicts rapid proliferation of AI into core public systems. In utilities, autonomous predictive maintenance combined with decentralized AI edge nodes will manage water, electricity, and waste systems with fewer human touchpoints and significantly faster response times. Transportation networks, on the other hand, will rely on AI-controlled routing, vehicle-to-infrastructure communications, and real-time traffic behavior modeling, reducing congestion and lowering emissions.

The future smart city moves beyond connected IoT nodes and into a responsive urban layer: sensor-rich surfaces, AI-augmented public safety systems, and adaptive lighting and energy usage based on human behavior patterns. Each urban component becomes part of a self-optimizing digital ecosystem that prioritizes efficiency without sacrificing human oversight.

Machine and Data Ecosystems at Scale

In Nokia's vision, Physical AI will power complex machine ecosystems operating in tandem with vast data ecosystems. Intelligent machines-ranging from autonomous drones to factory co-robots-will not remain standalone. Instead, they will constitute interoperable networks, exchanging structured data streams through federated learning models and 6G infrastructure. These devices won't just execute instructions-they'll negotiate, adapt, and optimize in collective terms.

Likewise, data movement becomes strategic. Structuring, classifying, distributing, and monetizing machine-generated data requires AI to evolve not only as a processor but also as a data steward. Nokia sees value not just in the information itself, but in the relationship between granular physical events and macro-system outcomes.

Unified Standards: Where Security, Efficiency, and Performance Converge

As Nokia drives deployments in sectors reshaped by AI, a clear theme emerges: the convergence of traditionally siloed priorities. Security, once treated as a backend function, moves to the design layer. Service continuity and quality are embedded into the AI stack, with resilience engineered from inception. And performance is no longer measured by throughput alone but by how well AI systems integrate, adapt, and co-function with human and machine users alike.

Under the leadership of its CTO, Nokia envisions a technological landscape where adaptability, ethical design, and intelligent automation form foundational pillars-not aspirational goals. Physical AI doesn't just support transformation; it defines it.